The wide availability of advanced information and communication technology has made it possible for users to expect a much wider access to decision support. Since the context of decision making is not necessarily restricted to the office desktop; decision support facilities have to be provided through access to technology anywhere; anytime; and through a variety of mediums. The spread of e-services and wireless devices has increased accessibility to data; and in turn; influenced the way in which users make decisions while on the move; especially in time-critical situations. For example; on site decision support for fire weather forecasting during bushfires can include realtime evaluation of quality of local fire weather forecast in terms of accuracy and reliability. Such decision support can include simulated scenarios indicating the probability of fire spreading over nearby areas that rely on data collected locally at the scene and broader data from the regional and national offices. Decision Support Systems (DSS) available on mobile devices; which triage nurses can rely on for immediate; expert advice based on available information; can minimise delay in actions and errors in triage at emergency departments (Cowie & Godley; 2006). Time-critical decision making problems require context-dependent metrics for representing expected cost of delaying an action (Greenwald & Dean; 1995); expected value of revealed information; expected value of displayed information (Horvitz; 1995) or expected quality of service (Krishnaswamy; Loke; & Zaslavsky; 2002). Predicting utility or value of information or services is aimed at efficient use of limited decision making time or processing time and limited resources to allow the system to respond to the time-critical situation within the required time frame. Sensitivity analysis (SA) pertains to analysis of changes in output due to changes in inputs (Churilov et al.;1996). In the context of decision support; traditionally SA includes the analysis of changes in output when some aspect of one or more of the decision model’s attributes change; and how these affect the final DSS recommendations (Triantaphyllou & Sanchez; 1997). In time-critical decision making monitoring; the relationship between the changes in the current input data and how these changes will impact on the expected decision outcome can be an important feature of the decision support (Hodgkin; San Pedro; & Burstein; 2004; San Pedro; Burstein; Zaslavsky; & Hodgkin; 2004). Thus; in a time-critical decision making environment; the decision maker requires information pertaining to both the robustness of the current model and ranking of feasible alternatives; and how sensitivity this information is to time; for example; whether in 2; 5; or 10 minutes; a different ranking of proposed solutions may be more relevant. The use of graphical displays to relay the sensitivity of a decision to changes in parameters and the model’s sensitivity to time has been shown to be a useful way of inviting the decision maker to fully investigate their decision model and evaluate the risk associated with making a decision now (whilst connectivity is possible); rather than at a later point in time (when perhaps a connection has been lost) (Cowie & Burstein; 2006). In this article; we present an overview of the available approaches to mobile decision support and specifically highlight the advantages such systems bring to the user in time-critical decision situations. We also identify the challenges that the developers of such systems have to face and resolve to ensure efficient decision support under uncertainty is provided.